Abstract
In this article, attention is paid to the task of remote monitoring of the state of an industrial roller using radar. A millimeter-wave radar using an FMCW signal is used to assess the state of the roller rotation quality. A test bench has been developed and a dataset of test signals has been recorded. An approach to the analysis of recorded signals using digital processing based on Morlet wavelet and a deep learning neural network is proposed.






Similar content being viewed by others
Data Availability
Not Applicable.
References
Radar systems for modern civilian applications: Part 1. IEEE Signal Processing Magazine. 36 4, (2019).
Radar systems for modern civilian applications: Part 2. IEEE Signal Processing Magazine. 36 5, (2019).
Gradzki R, Kulesza Z, Bartoszewiczoster B. Method of shaft crack detection based on squared gain of vibration amplitude. Nonlinear Dyn. 2019;98:671–90.
Ma H, Zhao Q, Han Q, Wen B. Dynamic characteristics analysis of a rotor–stator system under different rubbing forms. Appl Math Model. 2015;39:2392–408.
Patel TH, Darpe AK. Vibration response of misaligned rotors. J Sound Vib. 2009;325:609–28.
Bharadwaj R, et al. 2013 Condition monitoring using standoff vibration sensing radar. AHS Airworthiness, CBM, and HUMS Specialists' Meeting, Huntsville, AL.
Ciattaglia G, et al. Performance evaluation of vibrational measurements through mmWave automotive radars. Remote Sens. 2021. https://doi.org/10.3390/rs13010098.
AWR1642 Evaluation module (AWR1642BOOST) single-chip mmwave sensing solution user’s guide, Texas instruments, (2020).
Vityazev S., Valuyskiy D. Experimental study of the industrial shaft uneven rotation influence on the characteristics of probing radar signals. 2023 25th International Conference on Digital Signal Processing and its Applications (DSPA).
PK. Sahu, RN. Rai. Effect of time-frequency representations for fault classification of rolling bearing in noisy conditions using deep learning. 2023 25th International Conference on Digital Signal Processing and its Applications (DSPA).
Brian Russell, Jiajun Han. 2016 Jean morlet and the continuous wavelet transform. CREWES Research Report.
Zeintl C., Eibensteiner F., Langer J. 2019 Evaluation of FMCW radar for vibration sensing in industrial environments. In 29th International Conference Radioelektronika (RADIOELEKTRONIKA). IEEE.
Khablov, D. 2021 Signal processing of doppler microwave vibration sensors with quadrature transformation. In 23rd International Conference on Digital Signal Processing and its Applications (DSPA). IEEE.
Patole SM, Torlak M, Wang D, Ali M. Automotive radars: a review of signal processing techniques. Signal Process Mag. 2017;34(2):22–35.
Skolnik Merrill. Radar handbook. 3rd ed. New York: McGraw Hill Companies; 2008.
Orkisz M, Szewczuk A. Spectrum shape based roller bearing fault detection and identification. IEEE Trans Ind Appl. 2023;59(2):1547–56.
E. Landi et al., 2023. "A mobile net neural network model for fault diagnosis in roller bearings," 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Kuala Lumpur, Malaysia,
Tang L, Wu X, Wang D, Liu X. A comparative experimental study of vibration and acoustic emission on fault diagnosis of low-speed bearing. IEEE Trans Instrum Meas. 2023;72:1–11.
Funding
Not Applicable.
Author information
Authors and Affiliations
Contributions
Denis Valuyskiy has done the soft implementation. Sergey Vityazev and Denis Valuyskiy have completed the experimental part and wrote the manuscript. Vladimir Vityazev has reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Research Involving Human and /or Animals.
Not Applicable.
Informed Consent.
Not Applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Advance in Artificial Intelligence for Machine Vision Applications” guest edited by Koushlendra Kumar Singh, B. Ramchandra Reddy, V. M. Gadre and Akbar Sheikh Akbari.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Valuyskiy, D., Vityazev, S. & Vityazev, V. Wavelet Radar Signal Processing and its Applications to Industrial Roller Rotation Monitoring. SN COMPUT. SCI. 5, 587 (2024). https://doi.org/10.1007/s42979-024-02897-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-024-02897-z